A Quantum Annealing-Based Approach to Extreme Clustering
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In this age of data abundance, there is a growing need for algorithms and techniques for clustering big data in an accurate and efficient manner. Well-known clustering methods of the past are computationally expensive, especially when employed to cluster massive datasets into a relatively large number of groups. The particular task of clustering millions (billions) of data points into thousands (millions) of clusters is referred to as extreme clustering. We have devised a distributed method, capable of being powered by a quantum processor, to tackle this clustering problem.
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